The world we live in is awash with data that comes pouring in from everywhere around us.
On its own this data is just noise and confusion.
To make sense of data, to find the meaning in it, we need the powerful branch of science - statistics.
Believe me there's nothing boring about statistics.
Especially not today when we can make the data sing.
With statistics we can really make sense of the world.
And there's more.
With statistics, the data deluge, as it's being called, is leading us
to an ever greater understanding of life on Earth and the universe beyond.
And thanks to the incredible power of today's computers,
it may fundamentally transform the process of scientific discovery.
I kid you not, statistics is now the sexiest subject around.
Did you know that there is one million boats in Sweden?
That's one boat per nine people!
It's the highest number of boats per person in Europe!
Being a statistician, you don't like telling your profession at dinner parties.
But really, statisticians shouldn't be shy
because everyone wants to understand what's going on.
And statistics gives us a perspective on the world we live in
that we can't get in any other way.
Statistics tells us whether the things we think and believe are actually true.
And statistics are far more useful than we usually like to admit.
In the last recession there was this famous call-in to a talk radio station.
The man complained, "In times like this when unemployment rates are up to 13%, income has fallen by 5%,
"and suicide rates are climbing, and I get so angry that the government
"is wasting money on things like collection of statistics."
I'm not officially a statistician.
Strictly speaking, my field is global health.
But I got really obsessed with stats when I realised how much people
in Sweden just don't know about the rest of the world.
I started in our medical university, Karolinska Institutet,
an undergraduate course called Global Health.
These students coming to us actually have the highest grade you can get
in the Swedish college system,
so I thought, "Maybe they know everything I'm going to teach them."
So I did a pre-test when they came, and one of the questions
from which I learned a lot was this one -
which country has the highest child mortality of these five pairs?
I won't put you at test here, but it is Turkey
which is highest there, Poland,
Russia, Pakistan, and South Africa.
And these were the result of the Swedish students.
A 1.8 right answer out of five possible.
And that means there was a place for a professor of International Health and for my course.
But one late night when I was compiling the report, I really realised my discovery.
I had shown that Swedish top students know statistically
significantly less about the world than the chimpanzees.
Because the chimpanzees would score half right.
If I gave them two bananas with Sri Lanka and Turkey,
they would be right half of the cases, but the students are not there.
I did also an unethical study of the professors of the Karolinska Institutet,
that hands out the Nobel Prize for medicine, and they are on par with the chimpanzees there.
Today there's more information accessible than ever before.
'And I work with my team at the Gapminder Foundation
'using new tools that help everyone make sense of the changing world.
'We draw on the masses of data that's now freely available
'from international institutions like the UN and the World Bank.
'And it's become my mission to share the insights
'from this data with anyone who'll listen, and to reveal how statistics is nothing to be frightened of.'
I'm going to provide you a view of
the global health situation across mankind.
And I'm going to do that in hopefully an enjoyable way, so relax.
So we did this software which displays it like this.
Every bubble here is a country -
this is China, this is India.
The size of the bubble is the population.
I'm going to stage a race between this sort of yellowish Ford here
and the red Toyota down there and the brownish Volvo.
The Toyota has a very bad start down here, and United States,
Ford is going off-road there,
and the Volvo is doing quite fine, this is the war.
The Toyota got off track, now Toyota is on the healthier side of Sweden.
That's about where I sold the Volvo and bought the Toyota.
AUDIENCE LAUGH
This is the great leap forward, when China fell down.
It was the central planning by Mao Zedong.
China recovered and said, "Never more stupid central planning,"
but they went up here.
No, there is one more inequity, look there - United States
They broke my frame. Washington DC is so rich over there,
but they are not as healthy as Kerala in India. It's quite interesting, isn't it?
LAUGHTER AND APPLAUSE
Welcome to the USA, world leaders in big cars
and free data.
There are many here who share my vision of making public data accessible and useful for everyone.
The city of San Francisco is in the lead, opening up its data on everything.
Even the police department is releasing all its crime reports.
This official crime data has been turned
into a wonderful interactive map by two of the city's computer whizzes.
It's community statistics in action.
Crimespotting is a map of crime reports from the San Francisco Police Department
showing dots on maps for citizens to be able to see
patterns of crime around their neighbourhoods in San Francisco.
The map is not just about individual crimes but about broader patterns that show you where crime is
clustered around the city, which areas have high crime,
and which areas have relatively low crime.
We're here at the top of Jones Street on Nob Hill...
..quite a nice neighbourhood.
What the crime maps show us is the relationship between
topography and crime.
Basically the higher up the hill, the less crime there is.
You cross over the border
into the flats...
Essentially as soon as you get into the lower lying areas of Jones Street the crime just skyrockets.
We're here in the uptown Tenderloin district.
It's one of the oldest and densest neighbourhoods in San Francisco.
This is where you go to buy drugs.
Right around here.
We see lots of aggravated assaults, lots of auto thefts.
Basically a huge part of the crime that happens in the city happens in this five or six block radius.
If you've been hearing police sirens in your neighbourhood,
you can use the map to find out why.
If you're out at night in an unfamiliar part of town,
you can check the map for streets to avoid.
If a neighbour gets burgled, you can see -
is it a one-off or has there been a spike in local crime?
If you commute through a neighbourhood and you're worried
about its safety, the fact that we have the ability to turn off all
the night-time and middle-of-the-day crimes
and show you just the things that are happening during the commute,
it is a statistical operation. But I think to people that are interacting with the thing
it feels very much more like they're just sort of browsing a website or shopping on Amazon.
They're looking at data and they don't realise they're doing statistics.
What's most exciting for me is that public statistics
is making citizens more powerful and the authorities more accountable.
We have community meetings that the police attend
and what citizens are now doing are bringing printouts
of the maps that show where crimes are taking place,
and they're demanding services from the police department
and the police department is now having to change how they police,
how they provide policing services,
because the data is showing what is working and what is not.
People in San Francisco are also using public data
to map social inequalities and see how to improve society.
And the possibilities are endless.
I think our dream government data analysis project
would really be focused on live information,
on stuff that was being reported and pushed out to the world over the internet as it was happening.
You know, trash pickups, traffic accidents, buses,
and I think through the kind of stats-gathering power
of the internet it's possible to really begin to see the workings of the city
displayed as a unified interface.
So that's where we are heading.
Towards a world of free data with all the statistical insights that come from it,
accessible to everyone, empowering us as citizens and letting us hold our rulers to account.
It's a long way from where statistics began.
Statistics are essential to us to monitor our governments and our societies.
But it was our rulers up there who started
the collection of statistics in the first place in order to monitor us!
In fact the word 'statistics' comes from 'the state'.
Modern statistics began two centuries ago.
Once it got going, it spread and never stopped.
And guess who was first!
The Chinese have Confucius, the Italians have da Vinci,
and the British have Shakespeare.
And we have the Tabellverket -
the first ever systematic collection of statistics!
Since the year 1749 we have collected data
on every birth, marriage and death, and we are proud of it!
The Tabellverket recorded information
from every parish in Sweden.
It was a huge quantity of data and it was the first time any government
could get an accurate picture of its people.
Sweden had been the greatest military power in Northern Europe,
but by 1749 our star was really fading
and other countries were growing stronger.
At least we were a large power,
thought to have 20 million people, enough to rival Britain and France.
But we were in for a nasty surprise.
The first analysis of the Tabellverket
revealed that Sweden only had two million inhabitants.
Sweden was not just a power in decline, it also had a very small population.
The government was horrified by this finding - what if the enemy found out?
But the Tabellverket also showed that many women died in childbirth and many children died young.
So government took action to improve the health of the people.
This was the beginning of modern Sweden.
It took more than 50 years before the Austrians, Belgians, Danes,
Dutch, French, Germans, Italians
and, finally, the British, caught up with Sweden in collecting and using statistics.
It was called political arithmetic. It was a lovely phrase that was used for statistics.
Governments could have much more control and understanding of
the society - how it was working, how it was developing
and essentially so they could control it better.
It wasn't just governments who woke up to the power of statistics.
Right across Europe, 19th century society went mad for facts.
And, despite its late start, Britain,
with its Royal Statistical Society in London,
was soon a statisticians' nirvana.
I love looking at old copies of the Royal Statistical Society journal
because it's full of such odd stuff.
There's a wonderful paper from the 1840s
which shows a map of England and the rates of bastardy in each county.
So you can identify very quickly the areas with high rates of bastardy.
Being in East Anglia it always makes me slightly laugh that Norfolk
seems to top the "bastardy league" in the 1840s.
One of the founders of the Royal Statistical Society
was the great Victorian mathematician and inventor Charles Babbage.
In 1842 he read the latest poem by an equally great Victorian, Alfred Tennyson.
Vision of Sin contained the lines:
"Fill the cup, and fill the can
"Have a rouse before the morn
"Every moment dies a man Every moment one is born."
So keen a statistician was Babbage that he could not contain himself.
He dashed off a letter to Tennyson
explaining that because of population growth,
the line should read,
"Every moment dies a man and one and a 16th is born."
I may add that the exact figure is 1.067,
but something must be conceded to the laws of metre.
In the 19th century, scholars all over Europe did amazing work
in measuring their societies.
They were hoovering up data on almost everything.
But numbers alone don't tell you anything.
You have to analyse them, and that's what makes statistics.
When the first statisticians began to get to grips with
analysing their data
they seized upon the average, and they took the average of everything.
What's so great about an average is that
you can take a whole mass of data and reduce it to a single number.
And though each of us is unique, our collective lives produce
averages that can characterise whole populations.
I looked in my local newspaper one week and saw a pensioner
had accidentally put her foot on the accelerator
and crushed her friend against a wall.
Devastating, hideous, horrible thing to happen.
And then there was a second one about a young man who didn't have
a driving licence, was driving a car under the influence of drugs and alcohol
and he bashed into a pedestrian and killed him.
What's remarkable, absolutely remarkable, if you look at the number
of people who die each year in traffic crashes, it's nearly a constant.
What?
All these individual events, somehow when you sum them all up there's the same number every year.
And every year, two and a half times as many men
die in traffic crashes as women, and it's a constant.
And every year the rate in Belgium is double the rate in England.
There are these remarkable regularities.
So that these individual particular events sum up into a social phenomenon.
Let's see what Sweden have done.
We used to boast about fast social progress, that's where we were....
'In my lectures, to tell stories about the changing world,
'I use the averages from entire countries,
'whether the average of income, child mortality, family size
'or carbon output.'
OK, I give you Singapore. The year I was born,
Singapore had twice the child mortality of Sweden, the most tropical country in the world,
a marshland on the Equator, and here we go.
It took a little time for them to get independent,
but then they started to grow their economy,
and they made the social investment, they got away malaria,
they got a magnificent health system that beat both US and Sweden.
We never thought it would happen that they would win over Sweden!
LAUGHTER AND APPLAUSE
But useful as averages are, they don't tell you the whole story.
On average, Swedish people have slightly less than two legs.
This is because few people only have one leg or no legs,
and no-one has three legs.
So almost everybody in Sweden has more than the average number of legs.
The variation in data is just as important as the average.
But how do you get a handle on variation?
For this, you transform numbers into shapes.
Let's look again at the number of adult women in Sweden
for different heights.
Plotting the data as a shape shows how much their heights
vary from the average and how wide that variation is.
The shape a set of data makes is called its distribution.
This is the income distribution of China, 1970.
This is the income distribution of the United States, 1970.
Almost no overlap, and what has happened?
China is growing, it's not so equal any longer,
and it's appearing here overlooking the United States.
Almost like a ghost, isn't it?
It's pretty scary.
Rrrr!
LAUGHTER
The statisticians who first explored distribution
discovered one shape that turned up again and again.
The Victorian scholar Francis Galton
was so fascinated he built a machine that could reproduce it,
and he found it fitted so many different sets of measurements
that he named it the normal distribution.
Whether it was people's arm spans, lung capacities,
or even their exam results,
the normal distribution shape recurred time and time again.
Other statisticians soon found many other regular shapes,
each produced by particular kinds of natural or social processes.
And every statistician has their favourite.
The Poisson distribution, the Poisson shape is my favourite distribution.
I think it's an absolute cracker.
The Poisson shape describes how likely it is
that out-of-the-ordinary things will happen.
Imagine a London bus stop where we know that on average
we'll get three buses in an hour.
We won't always get three buses, of course.
Amazingly, the Poisson shape will show us the probability
that in any given hour we will get four, five, or six buses,
or no buses at all.
The exact shape changes with the average.
But whether it's how many people will win the lottery jackpot
each week,
or how many people will phone a call centre each minute,
the Poisson shape will give the probabilities.
The wonderful example where this was applied to in the late 19th century
was to count each year the number of Prussian officers,
cavalry officers, who were kicked to death by their horses.
Now, some years there were none, some years there were one,
some years there were two, up to seven, I think, one particularly bad year.
But with this distribution, however many years there were
with nought, one, two, three, four Prussian cavalry officers
kicked to death by their horses, beautifully obeyed the Poisson distribution.
So statisticians use shapes to reveal the patterns in the data.
But we also use images of all kinds
to communicate statistics to a wider public.
Because if the story in the numbers
is told by a beautiful and clever image, then everyone understands.
Of the pioneers of statistical graphics, my favourite is Florence Nightingale.
There are not many people who realise that she was known
as a passionate statistician and not just the Lady of the Lamp.
She said that "to understand God's thoughts, we must study statistics,
"for these are the measure of His purpose."
Statistics was for her a religious duty and moral imperative.
When Florence was nine years old she started collecting data.
Her data was different fruits and vegetables she found.
Put them into different tables.
Trying to organise them in some standard form.
And so we have one of Nightingale's first statistical tables
at the age of nine.
In the mid 1850s Florence Nightingale went to the Crimea to care for British casualties of war.
She was horrified by what she discovered.
For all the soldiers being blown to bits on the battlefield, there were many, many more soldiers
dying from diseases they caught in the army's filthy hospitals.
So Florence Nightingale began counting the dead.
For two years she recorded mortality data in meticulous detail.
When the war was over she persuaded the government to set up
a Royal Commission of Inquiry,
and gathered her data in a devastating report.
What has cemented her place in the statistical history books
are the graphics she used.
And one in particular, the polar area graph.
For each month of the war, a huge blue wedge represented
the soldiers who had died from preventable diseases.
The much smaller red wedges were deaths from wounds,
and the black wedges were deaths from accidents and other causes.
Nightingale's graphics were so clear they were impossible to ignore.
The usual thing around Florence Nightingale's time
was just to produce tables and tables of figures - absolutely really tedious stuff that,
unless you're an absolutely dedicated statistician,
it's really quite difficult to spot the patterns quite naturally.
But visualisations, they tell a story, they tell a story immediately.
And the use of colour and the use of shape can really tell a powerful story.
And nowadays of course we can make things move as well.
Florence Nightingale would have loved to have played with...
She would have produced wonderful animations, I'm absolutely certain of it.
Today, 150 years on, Nightingale's graphics
are rightly regarded as a classic.
They led to a revolution in nursing, health care
and hygiene in hospitals worldwide, which saved innumerable lives.
And statistical graphics has become an art form of its very own,
led by designers who are passionate about visualising data.
This is the Billion Pound-O-Gram.
This image arose out of frustration
with the reporting of billion pound amounts in the media.
£500 billion pounds for this war.
£50 billion for this oil spill.
It doesn't make sense - the numbers are too enormous to get your mind round.
So I scraped all this data from various news sources and created this diagram.
So the squares here are scaled according to the billion pound amounts.
When you see numbers visualised like this
you start to have a different relationship with them.
You can start to see the patterns, and the scale of them.
Here in the corner, this little square - £37 billion.
This was the predicted cost of the Iraq war in 2003.
As you can see it's grown exponentially over the last few years
and the total cost now is around about £2,500 billion.
It's funny because when you visualise statistics
you understand them, and when you understand them
you can really start to put things in perspective.
Visualisation is right at the heart of my own work too.
I teach global health.
And I know having the data is not enough -
I have to show it in ways people both enjoy and understand.
Now I'm going to try something I've never done before.
Animating the data in real space,
with a bit of technical assistance from the crew.
So here we go.
First, an axis for health.
Life expectancy from 25 years to 75 years.
And down here an axis for wealth.
Income per person - 400, 4,000, 40,000.
So down here is poor and sick.
And up here is rich and healthy.
Now I'm going to show you the world
200 years ago, in 1810.
Here come all the countries.
Europe, brown; Asia, red; Middle East, green;
Africa south of the Sahara, blue; and the Americas, yellow.
And the size of the country bubble shows the size of the population.
In 1810, it was pretty crowded down there, wasn't it?
All countries were sick and poor.
Life expectancy was below 40 in all countries.
And only UK and the Netherlands were slightly better off. But not much.
And now I start the world.
The industrial revolution makes countries in Europe and elsewhere
move away from the rest.
But the colonized countries in Asia and Africa,
they are stuck down there.
And eventually the Western countries get healthier and healthier.
And now we slow down to show the impact of the First World War
and the Spanish flu epidemic.
What a catastrophe!
And now I speed up through the 1920s and the 1930s and,
in spite of the Great Depression,
Western countries forge on towards greater wealth and health.
Japan and some others try to follow.
But most countries stay down here.
And after the tragedies of the Second World War,
we stop a bit to look at the world in 1948.
1948 was a great year.
The war was over,
Sweden topped the medal table at the Winter Olympics and I was born.
But the differences between the countries of the world
was wider than ever.
United States was in the front.
Japan was catching up.
Brazil was way behind,
Iran was getting a little richer from oil but still had short lives.
And the Asian giants...
China, India, Pakistan, Bangladesh, and Indonesia,
they were still poor and sick down here.
But look what was about to happen! Here we go again.
In my lifetime, former colonies gained independence and then finally
they started to get healthier and healthier and healthier.
And in the 1970s, then countries in Asia and Latin America
started to catch up with the Western countries.
They became the emerging economies.
Some in Africa follows,
some Africans were stuck in civil war, and others were hit by HIV.
And now we can see the world in the most up-to-date statistics.
Most people today live in the middle.
But there is huge difference at the same time
between the best-off countries and the worst-off countries.
And there are also huge inequalities within countries.
These bubbles show country averages but I can split them.
Take China. I can split it into provinces.
There goes Shanghai...
It has the same health and wealth as Italy today.
And there is the poor inland province Guizhou,
it is like Pakistan.
And if I split it further, the rural parts are like Ghana in Africa.
And yet, despite the enormous disparities today,
we have seen 200 years of remarkable progress!
That huge historical gap between the west and the rest is now closing.
We have become an entirely new, converging world.
And I see a clear trend into the future.
With aid, trade, green technology and peace,
it's fully possible that everyone can make it
to the healthy, wealthy corner.
Well, what you've just seen in the last few minutes
is a story of 200 countries shown over 200 years and beyond.
It involved plotting 120,000 numbers.
Pretty neat, huh?
So, with statistics, we can begin to see things as they really are.
From tables of data to averages, distributions and visualisations,
statistics gives us a clear description of the world.
But, with statistics, we can not only discover WHAT is happening
but also explore WHY,
by using the powerful analytical method - correlation.
Just looking at one thing at a time doesn't tell you very much.
You've got to look at the relationships between things,
how they change, how they vary together.
That's what correlation is about.
That's how you start trying to understand the processes
that are really going on in the world and society.
Most of us today would recognise that crime correlates to poverty,
that infection correlates to poor sanitation,
and that knowledge of statistics correlates
to being great at dancing!
Correlations can be very tricky.
I got a joke about silly correlations.
There was this American who was afraid of heart attack.
He found out that the Japanese ate very little fat
and almost didn't drink wine,
but they had much less heart attacks than the Americans.
But, on the other hand, he also found out that the French
eat as much fat as the Americans and they drink much more wine but they also have less heart attacks.
So he concluded that what kills you is speaking English.
# Smoke, smoke, smoke that cigarette
# Puff, puff, puff and if you smoke yourself to death... #
The time, the pace, the cigarette. Weights Tilt.
The best example of a really ground-breaking correlation
is the link that was established in the 1950s between smoking and lung cancer.
Not long after the Second World War, a British doctor, Richard Doll,
investigated lung cancer patients in 20 London hospitals.
And he became certain that the only thing they had in common was smoking.
So certain, that he stopped smoking himself.
But other people weren't so sure.
A lot of the discussion of the early data,
linking smoking to lung cancer, said, "It's not the smoking, surely,
"that thing we've done all our lives, that can't be bad for you.
"Maybe it's genes.
"Maybe people who are genetically predisposed to get lung cancer
"are also genetically predisposed to smoke."
"Maybe it's not the smoking, maybe it's air pollution -
"that smokers are somehow more exposed to air pollution than non-smokers.
"Maybe it's not smoking, maybe it's poverty."
So now we've got three alternative explanations, apart from chance.
To verify his correlation did imply cause and effect.
Richard Doll created the biggest statistical study of smoking yet.
He began tracking the lives of 40,000 British doctors,
some of whom smoked and some of whom didn't,
and gathered enough data
to correlate the amount the doctors smoked
with their likelihood of getting cancer.
Eventually, he not only showed a correlation between smoking and lung cancer,
but also a correlation between stopping smoking and reducing the risk.
This was science at its best.
What correlations do not replace is human thought.
You've got to think about what it means.
What a good scientist does, if he comes with a correlation,
is try as hard as she or he possibly can to disprove it,
to break it down, to get rid of it, to try and refute it.
And if it withstands all those efforts at demolishing it
and it is still standing up then, cautiously, you say, "We really might have something here."
However brilliant the scientist, data is still the oxygen of science.
The good news is that the more we have, the more correlations we'll find, the more theories we'll test,
and the more discoveries we're likely to make.
And history shows how our total sum of information grows in huge leaps as we develop new technologies.
The invention of the printing press kicked off the first data and information explosion.
If you piled up all the books that had been printed by the year 1700,
they would make 60 stacks each as high as Mount Everest.
Then, starting in the 19th century,
there came a second information revolution with the telegraph,
gramophone and camera. And later radio and TV.
The total amount of information exploded.
And by the 1950s the information available to us all had multiplied 6,000 times.
Then, thanks to the computer and later the internet, we went digital.
And the amount of data we have now is unimaginably vast.
A single letter printed in a book is equivalent to a byte of data.
A printed page equals a kilobyte or two.
Five megabytes is enough for the complete works of Shakespeare.
10 gigabytes - that's a DVD movie.
Two terabytes is the tens of millions of photos added to Facebook every day.
Ten petabytes is the data recorded every second by the world's largest particle accelerator.
So much only a tiny fraction is kept.
Six exabytes is what you'd have if you sequenced the genomes of every single person on Earth.
But really, that's nothing.
In 2009, the internet added up to 500 exabytes.
In 2010, in just one year, that will double to more than one zettabyte!
Back in the real world, if we turned all this data into print it would make 90 stacks of books,
each reaching from here all the way to the sun!
The data deluge is staggering, but, with today's computers
and statistics, I'm confident we can handle it.
When it comes to all the data on the internet,
the powerhouse of statistical analysis
is the Silicon Valley giant Google.
The average person over their lifetime is exposed to about 100 million words of conversation.
And so if you multiple that by the six billion people on the planet,
that amount of words is about equal to the number of words
that Google has available at any one instant in time.
Google's computers hoover up and file away every document, web page, and image they can find.
They then hunt for patterns and correlations in all this data,
doing statistics on a massive scale.
And, for me, Google has one project that's particularly exciting - statistical language translation.
We wanted to provide access to all the web's information, no matter what language you spoke.
There's just so much information on the internet,
you couldn't hope to translate it all by hand into every possible language.
We figured we'd have to be able to do machine translation.
In the past, programmers tried to teach their computers
to see each language as a set of grammatical rules - much like the way languages are taught at school.
But this didn't work because no set of rules could capture a language
in all its subtlety and ambiguity.
"Having eaten our lunch the coach departed."
Well, that's obviously incorrect.
Written like that it would imply that the coach has eaten the lunch.
It would be far better to say...
"having eaten our lunch we departed in the coach."
Those rules are helpful and they are useful most of time, but they don't turn out to be true all the time.
And the insight of using statistical machine translation is saying,
"If you've got to have all these exceptions anyways, maybe you can get by without having any of the rules.
"Maybe you can treat everything as an exception." And that's essentially what we've done.
What the computer is doing when he's learning how to translate
is to learn correlations between words
and correlations between phrases.
So we feed the system very large amounts of data
and then the system is seeing that a certain word or a certain phrase
correlates very often to the other language.
Google's website currently offers translation between any of 57 different languages.
It does this purely statistically, having correlated a huge collection of multilingual texts.
The people that built the system don't need to know Chinese
in order to build the Chinese-to-English system, or they don't need to know Arabic.
But the expertise that's needed is basically knowledge of statistics,
knowledge of computer science, knowledge of infrastructure
to build those very large computational systems that we are building for doing that.
I hooked up with Google from my office in Stockholm to try the translator for myself.
'I will type... some Swedish sentences.'
OK.
Sveriges...
..guldring i orat.
OK. So it says, "Sweden's finance minister has a ponytail and a gold ring in your ear."
I guess it probably means in his ear. 'That's exactly correct, it's amazing!
'He comes from the Conservative party, that's the kind of Sweden we have today.
'I will type one more sentence.'
'I sitt samkonade...'
partnerskap...
nya biskop.
"In his same-sex partnership has Stockholm's new bishop and his partners a three-year son."
It's almost perfect, there's one important thing -
it's HER, it's a lesbian partnership.
OK, so those kinds of words his and her are one of the challenges
in translation to get really those right.
Especially when it comes to bishops one can excuse it!
'Right, right.'
I guess more often than not it would probably be a "his". 'I will write one more sentence.'
Nar Sverige deltar I olympiader ar malet
'inte att vinna utan att sla Norge.'
OK. "When Sweden is taking part in Olympic goal is not to win but to beat Norway."
'Yes! This is what it is!
'But they are very good in Winter Olympics, so we can't make it, but we are trying.'
Ah, very good, very good.
'This is absolutely amazing, you know, and I was especially impressed
'that it picks up words like "same-sex partnership" which are very new to the language."
'The translator is good, but if they succeed with what's next, that'll be remarkable.'
One of the exciting possibilities
is combining the machine translation technology with the speech recognition technology.
Now, both of these are statistical in nature.
The machine translation relies on the statistics of mapping from one language to another,
and similarly speech recognition relies on the statistics of mapping from a sound form to the words.
When we put them together,
now we have the capability of having instant conversation
between two people that don't speak a common language.
I can talk to you in my language,
you hear me in your language and you can answer back.
And in real time we can make that translation,
we can bring two people together and allow them to speak.
The internet is just one of many technologies created to gather massive amounts of data.
Scientists studying our earth and our environment
now use an incredible range of instruments
to measure the processes of our planet.
All around us are sensors continuously measuring temperature, water flow, and ocean currents.
And high in orbit are satellites busy imaging cloud formations, forest growth and snow cover.
Scientists speak of "instrumenting the earth".
And pointing up to the skies above are powerful new telescopes mapping the universe.
What's happening in astronomy is typical of how profoundly
this new torrent of data is transforming science.
Astronomers are now addressing many enduring mysteries of the cosmos
by applying statistical methods to all this new data.
The galaxy is a very big place and it's got billions of stars in it,
and so to put together a coherent picture of the whole galaxy requires having an enormous amount of data.
And before you could do a large sky survey with sensitive, digital detectors
that meant that you could map many, many stars all at once,
it was very difficult to build up enough data on enough of the galaxy.
In the past, large surveys of the night sky had to be done
by exposing thousands of large photographic plates.
But these surveys could take 25 years or more to complete.
Then, in the 1990s, came digital astronomy and a huge increase
in both the amount and the accessibility of data.
The Sloan Sky Survey is the world's biggest yet, using a massive digital sensor
mounted on the back of a custom-built telescope in New Mexico.
It's scanned the sky night after night for eight years,
building up a composite picture in unprecedented resolution.
The Sloan is some of the best, deepest survey data that we have in astronomy.
Both on our own galaxy and on galaxies further away from ours.
All the Sloan data is on the internet,
and with it astronomers have identified millions of hitherto unknown stars and galaxies.
They also comb the database for statistical patterns
which will prove, disprove, or even suggest new theories.
So we have this idea that galaxies grow, they become large galaxies like the one we live in, the milky way,
not all at once, or not smoothly, but by continuously incorporating,
basically cannibalising, smaller galaxies.
They dissolve them and they become part of the bigger galaxy as it grows.
It's a startling idea, and, in the Sloan data, is the evidence to support it.
Groups of stars that came from cannibalised galaxies
stand out in the Sloan data as statistically different from other stars
because they move at a different velocity.
Each big spike on one of these distribution graphs
means Professor Rockosi has found a group of stars all travelling in a different way to the rest.
They are the telltale patterns she's looking for.
The evidence is accumulating that, in fact, this really is how galaxies grow,
or an important way in which how galaxies grow.
And so this is an important part of understanding how galaxies form, not only ours but every galaxy.
The more data there is, the more discoveries can be made.
And the technology is getting better all the time.
The next big survey telescope starts its work in 2015.
It will leave Sloan in the dust!
Sloan has taken eight years to cover one quarter of the night sky.
The new telescope will scan the entire sky, in even greater resolution, every three days!
The vast amounts of data we have today allows researchers in all sorts of fields
to test their theories on a previously unimaginable scale.
But more than this, it may even change the fundamental way science is done.
With the power of today's computers applied to all this data,
the machines might even be able to guide the researchers.
We're at a potentially profoundly important
and potentially one of the most significant points in science,
and certainly one of the most exciting,
where the potential to transform not just how scientists do science but even what science is possible.
And what will power that transformation
of both how science is done and even what science is possible
is going to be computation.
Many of the dynamics of the natural world, like the interplay between the rainforests and the atmosphere,
are so complex that we don't as yet really understand them.
But now computers are generating literally tens of thousands of different simulations
of how these biological systems might work.
It's like creating thousands of hypothetical parallel worlds.
Each and every one of these simulations
is analysed with statistics to see if any are a good match for what is observed in nature.
The computers can now automatically generate,
test and discard hypotheses with scarcely a human in sight.
This new application of statistics will become absolutely vital for the future of science.
It's creating a new paradigm, if you like,
in science, in the way in which we can do science,
which is increasingly...
Which one might characterise as... data-centric or data driven
rather than being hypothesis-driven or experimentally-driven.
So, it's exciting times in terms of the science,
in terms of the computation and in terms of the statistics.
Now, if all that sounds a bit abstract and theoretical to you, how about one final frontier?
Could statistics even make sense of your feelings?
In California - where else? - one computer scientist
is harvesting the internet to try to divine the patterns of our innermost thoughts and emotions.
This is the madness movement.
The madness movement represents a skyscraper view of the world.
Each of these brightly coloured dots is an individual feeling
expressed by someone out there in a blog or a tweet.
And when you click on the dot it explodes to reveal the underlying feeling of that person.
This is what people say they're feeling today.
Better...safe...
crappy...
well...
pretty...special...
sorry...alone...
So, every minute, We Feel Fine crawls the world's blogs,
takes all the sentences that start with the words "I feel" or "I am feeling",
and puts them in a database.
We collect all the feelings and we count the most common.
They are better...bad...
good...right...
guilty...sick...
the same...like shit...
sorry...well...
and so on.
And we can take a look at any one feeling and analyse it.
Right now a lot of people are feeling happy.
We can take a look at all the people who are happy and break it down by age, gender or location.
Since bloggers have public profiles we have that information and so we can ask questions like,
"Are women happier than men?" or, "Is England happier than the United States?"
We find that, as people get older, they get happier.
And, moreover, we find that for younger people they associate happiness more with excitement,
and, as people get older, they associate happiness more with peacefulness.
And we also find that women feel loved more often than men, but also more guilty.
While men feel good more often than women, but also more alone.
As people lead more and more of their lives online, they leave behind digital traces,
and with these digital traces we can begin to statistically analyse what it means to be human.
So where does all of this leave us?
We generate unimaginable quantities of data about everything you can think of.
We analyse it to reveal the patterns.
And now not only experts but all of us can understand the stories in the numbers.
Instead of being led astray by prejudice,
with statistics at our fingertips, our eyes can be open for a fact-based view of the world.
So, more than ever before, we can become authors of our own destiny.
And that's pretty exciting isn't it?!
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LYRICS DEGENERATE INTO GIBBERISH
GIBBERISH DEGENERATES INTO NOISE
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